metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sparse-encoder
  - sparse
  - asymmetric
  - inference-free
  - splade
  - generated_from_trainer
  - dataset_size:9000
  - loss:SpladeLoss
  - loss:SparseMultipleNegativesRankingLoss
  - loss:FlopsLoss
  - dataset_size:89000
base_model: distilbert/distilbert-base-uncased
widget:
  - text: Blank Neoprene Water Bottle Coolies (Variety Color 10 Pack)
  - text: >-
      Dream Spa 3-way 8-Setting Rainfall Shower Head and Handheld Shower Combo
      (Chrome). Use Luxury 7-inch Rain Showerhead or 7-Function Hand Shower for
      Ultimate Spa Experience!
  - text: ¿Está disponible el nuevo iPhone 7 Plus?
  - text: >-
      Naipo Back Massager Massage Chair Vibrating Car Seat Cushion for Back,
      Neck, and Thigh with 8 Motor Vibrations 4 Modes 3 Speed Heating at Home
      Office Car
  - text: >-
      Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100%
      Long Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep
      Pocket Queen Sheets Fit Up to 15Inch (White Fitted Sheet)
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
  - query_active_dims
  - query_sparsity_ratio
  - corpus_active_dims
  - corpus_sparsity_ratio
model-index:
  - name: >-
      Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions
      tuples
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.3
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.58
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.76
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.3
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.19333333333333336
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.132
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.07600000000000001
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.58
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.66
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.76
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5302210774188797
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.45638095238095233
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4675385567218492
            name: Dot Map@100
          - type: query_active_dims
            value: 6.380000114440918
            name: Query Active Dims
          - type: query_sparsity_ratio
            value: 0.9997909704437966
            name: Query Sparsity Ratio
          - type: corpus_active_dims
            value: 813.6908569335938
            name: Corpus Active Dims
          - type: corpus_sparsity_ratio
            value: 0.9733408408055306
            name: Corpus Sparsity Ratio

Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples

This is a Asymmetric Inference-free SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: Asymmetric Inference-free SPLADE Sparse Encoder
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): Router(
    (sub_modules): ModuleDict(
      (query): Sequential(
        (0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast)
      )
      (document): Sequential(
        (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
        (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
      )
    )
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("monkeypostulate/inference-free-splade-distilbert-base-uncased-nq")
# Run inference
queries = [
    "\u00bfHay una s\u00e1bana de algod\u00f3n ajustada disponible en tama\u00f1o queen?",
]
documents = [
    'Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100% Long Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep Pocket Queen Sheets Fit Up to 15Inch (White Fitted Sheet)',
    'ArtSocket Shower Curtain Teal Rustic Shabby Country Chic Blue Curtains Wood Rose Home Bathroom Decor Polyester Fabric Waterproof 72 x 72 Inches Set with Hooks',
    'AFARER Case Compatible with Samsung Galaxy S7 5.1 inch, Military Grade 12ft Drop Tested Protective Case with Kickstand,Military Armor Dual Layer Protective Cover - Blue',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[13.2777,  7.2952,  2.9255]])

Evaluation

Metrics

Sparse Information Retrieval

Dataset: NanoMSMARCO
Evaluated with SparseInformationRetrievalEvaluator

Metric	Value
dot_accuracy@1	0.3
dot_accuracy@3	0.58
dot_accuracy@5	0.66
dot_accuracy@10	0.76
dot_precision@1	0.3
dot_precision@3	0.1933
dot_precision@5	0.132
dot_precision@10	0.076
dot_recall@1	0.3
dot_recall@3	0.58
dot_recall@5	0.66
dot_recall@10	0.76
dot_ndcg@10	0.5302
dot_mrr@10	0.4564
dot_map@100	0.4675
query_active_dims	6.38
query_sparsity_ratio	0.9998
corpus_active_dims	813.6909
corpus_sparsity_ratio	0.9733

Training Details

Training Dataset

Unnamed Dataset

Size: 89,000 training samples
Columns: query and document
Approximate statistics based on the first 1000 samples:
query document
type string string
details
min: 8 tokens
mean: 21.52 tokens
max: 44 tokens

min: 8 tokens
mean: 33.4 tokens
max: 93 tokens

	query	document
type	string	string
details	min: 8 tokens mean: 21.52 tokens max: 44 tokens	min: 8 tokens mean: 33.4 tokens max: 93 tokens

Samples:

query	document
`¿Hay una lámpara de colgar con batería disponible?`	`Farmhouse Plug in Pendant Light with On/Off Switch Wire Caged Hanging Pendant Lamp 16ft Cord`
`¿Hay leggings con bolsillos disponibles para mujeres?`	`IUGA High Waist Yoga Pants with Pockets, Tummy Control, Workout Pants for Women 4 Way Stretch Yoga Leggings with Pockets`
`¿Cuál es la tapa de oscuridad marrón disponible?`	`Thicken It 100% Scalp Coverage Hair Powder - DARK BROWN - Talc-Free .32 oz. Water Resistant Hair Loss Concealer. Naturally Thicker Than Hair Fibers & Spray Concealers`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 0.003,
    "query_regularizer_weight": 0
}

Evaluation Dataset

Unnamed Dataset

Size: 1,000 evaluation samples
Columns: query and document
Approximate statistics based on the first 1000 samples:
query document
type string string
details
min: 8 tokens
mean: 20.94 tokens
max: 40 tokens

min: 8 tokens
mean: 33.09 tokens
max: 79 tokens

	query	document
type	string	string
details	min: 8 tokens mean: 20.94 tokens max: 40 tokens	min: 8 tokens mean: 33.09 tokens max: 79 tokens

Samples:

query	document
`¿Qué es un modelo anatómico del corazón?`	`Axis Scientific Heart Model, 2-Part Deluxe Life Size Human Heart Replica with 34 Anatomical Structures, Held Together with Magnets, Includes Mounted Display Base, Detailed Product Manual and Warranty`
`¿Hay un buscador de peces portátil disponible?`	`HawkEye Fishtrax 1C Fish Finder with HD Color Virtuview Display, Black/Red, 2" H x 1.6" W Screen Size`
`¿Hay un disfraz de Anna adulta de Frozen disponible para comprar?`	`Mitef Anime Cosplay Costume Princess Anna Fancy Dress with Shawl for Adult, L`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 0.003,
    "query_regularizer_weight": 0
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 2e-05
warmup_ratio: 0.1
batch_sampler: no_duplicates
router_mapping: {'query': 'query', 'answer': 'document'}

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {'query': 'query', 'answer': 'document'}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	NanoMSMARCO_dot_ndcg@10
0.5747	200	3.33	-
1.1494	400	2.755	-
-1	-1	-	0.5302

Framework Versions

Python: 3.9.6
Sentence Transformers: 5.1.0
Transformers: 4.55.0
PyTorch: 2.8.0
Accelerate: 1.10.0
Datasets: 4.0.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

FlopsLoss

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}